臺灣博碩士論文加值系統

臨床資料含有許多有用的醫療相關資訊，如果能夠分享其中蘊含的資訊，無論是對於病患或是醫師都會有所助益。但是臨床資料會隨著病人回診檢驗，而有所增加或改變，因此可能會有多重測量而造成難以分析病人全部臨床資料的問題。此研究基於案例式推理架構，提出多重測量案例式推理法來分析多重的臨床資料。可在多重測量的情況下，尋找相似的病患，主要在預測針對一年內第一次接受治療的肝癌病患復發的情形，我們將病患資料隨機分成四組，各組進行交叉驗證，並將四組結果平均分析。最後預測出的模組取決於四組平均準確值較好的表現。並分析比較傳統案例式推理及多重測量案例式推理的結果，根據標準差的結果，我們認為模組加入多重的臨床資料案例式推理的結果較傳統案例式推理能趨於穩定。多重測量案例式推理敏感度的平均值也較傳統案例式推理好，此研究於各個不同的組合參數如計算特徵的五種演算法，不同的臨床天數週期，不同的權重來計算預測模組。
此研究可提供檢索相似病患的預測模組給其他有需要的使用者，例如病患或是醫療人員以供參考。

Due to the progress of medicine, clinical data are increased very rapidly and biochemistry laboratory items are multiply measured with the subsequent consultations of patients. These multiple measurements clinical data may become another problem during analysis. This study proposes a practicable method to appropriately handle the clinical data with multiple measurements. Based on the case-based reasoning (CBR) method, we propose a multiple measurements CBR (MMCBR) method, extended from single measurement CBR (SingleCBR), for analyzing clinical data. The research target of this study is the prediction of recurrent status of liver cancer patients after receiving the first treatment in one year. We randomly separated dataset into four subsets, and the average results of classification using three-fold cross validation in four random datasets are analyzed, respectively. The results show models with better performance in the mean accuracy of four random datasets. Combination CBR could produce comparable results with SingleCBR and might have better stability than that of SingleCBR according to the standard deviation of accuracy. The mean sensitivities of MMCBR and Combination CBR in most combinations are better than those of SingleCBR. In this study, five feature selection approaches, different time periods of clinical data merging, and different weights are examined for establishing a predictive model.

中文摘要 i
ABSTRACT ii
Chapter 1 Introduction 1
Chapter 2 Background and Related Work 4
2.1 Case-based reasoning 4
2.2 Case-based reasoning in the medical domain 4
2.3 Liver cancer related studies 6
Chapter 3 Method 8
3.1 Feature selection approaches 8
3.2 Case-based reasoning 10
3.3 Multiple measurements case-based reasoning (MMCBR) 11
3.3.1 The pair weight and case weight of MMCBR 12
3.4 Material 20
3.5 Evaluation 24
Chapter 4 Result 28
Chapter 5 Discussion 33
Chapter 6 Conclusion 35
Chapter 7 Future Work 36
Appendixes 37
A.1 Material 37
A.2 Evaluation 38
A.3 Result 39
A.4 Discussion 43
Reference 45

[1]J. F. De Paz, Dept. de Inf. y Autom., Univ. de Salamanca, S. Salamanca Rodriguez, J. Bajo, and J. M. Corchado, “CBR System for Diagnosis of Patients,” Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems, pp. 807-812, 2008.
[2]X. Zhou, H. Han, I. Chankai, A. Prestrud, and A. Brooks, “Approaches to text mining for clinical medical records,” in Proceedings of the 2006 ACM symposium on Applied computing, Dijon, France, 2006, pp. 235-239.
[3]J. C. Prather, D. F. Lobach, L. K. Goodwin, J. W. Hales, M. L. Hage, and W. E. Hammond, “Medical data mining: knowledge discovery in a clinical data warehouse,” Proc AMIA Annu Fall Symp, pp. 101-5, 1997.
[4]B. E. Himes, Y. Dai, I. S. Kohane, S. T. Weiss, and M. F. Ramoni, “Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records,” J Am Med Inform Assoc, vol. 16, no. 3, pp. 371-9, May-Jun, 2009.
[5]R.-H. Lin, and C.-L. Chuang, “A hybrid diagnosis model for determining the types of the liver disease,” Comput. Biol. Med., vol. 40, no. 7, pp. 665-670, 2010.
[6]R. H. Lin, “An intelligent model for liver disease diagnosis,” Artif Intell Med, vol. 47, no. 1, pp. 53-62, Sep, 2009.
[7]F. Davidoff, B. Haynes, D. Sackett, and R. Smith, “Evidence based medicine,” BMJ, vol. 310, no. 6987, pp. 1085-1086, 1995-04-29 08:00:00, 1995.
[8]A. Aamodt, and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approaches,” AI Commun., vol. 7, no. 1, pp. 39-59, 1994.
[9]M. M. Richter, and A. Aamodt, “Case-based reasoning foundations,” Knowl. Eng. Rev., vol. 20, no. 3, pp. 203-207, 2005.
[10]S. Shiu, and S. K. Pal, “Foundations of Soft Case-Based Reasoning,” 2004.
[11]H. Li, and J. Sun, “Ranking-order case-based reasoning for financial distress prediction,” Know.-Based Syst., vol. 21, no. 8, pp. 868-878, 2008.
[12]H. Karoui, R. Kanawati, and L. Petrucci, “COBRAS: cooperative CBR system for bibliographical reference recommendation,” in Proceedings of the 8th European conference on Advances in Case-Based Reasoning, Fethiye, Turkey, 2006, pp. 76-90.
[13]C. Chang Chunguang Chang, L. Wang Lijie Wang, Y. Liu Yachen Liu, and B. Gao, “CBR based expert system for house repair after earthquake,” CORD Conference Proceedings, vol. 2, pp. 1139-1143, 01/09, 2010.
[14]H. P. Nguyen, N. R. Prasad, D. H. Hung, and J. T. Drake, Approach to Combining Case Based Reasoning with Rule Based Reasoning for Lung Disease Diagnosis, p.^pp. 883-888: IFSA World Congress and 20th NAFIPS International Conference, 2001.
[15]M. B. Holger Storf, “Rule-based activity recognition framework: Challenges, technique and learning,” in PERVAS/VEHEALTH, 2009, pp. 1-7.
[16]I. Bichindaritz, and C. Marling, “Case-based reasoning in the health sciences: What''s next?,” Artif Intell Med, vol. 36, no. 2, pp. 127-35, Feb, 2006.
[17]A. Liaw, and M. Wiener, “Classification and Regression by randomForest,” R News, vol. 2, pp. 5, 2002.
[18]J. Zhang, J. Lu, and G. Zhang, “A hybrid knowledge-based prediction method for avian influenza early warning,” in Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics, San Antonio, TX, USA, 2009, pp. 617-622.
[19]M. Denis, and A. Jasmin, “Applying Case-Based Reasoning for Mobile Support in Diagnosing Infective Diseases,” CORD Conference Proceedings, pp. 779-783, 05/15, 2009.
[20]J. M. Juarez, M. Campos, A. Gomariz, J. Palma, and R. Marin, “A Reuse-Based CBR System Evaluation in Critical Medical Scenarios,” in Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence, 2009, pp. 261-268.
[21]L. Rokach, and O. Maimon, Data Mining and Knowledge Discovery Handbook pp. 165-192: Springer US, 2005.
[22]J. H. Wang, C. S. Changchien, T. H. Hu, C. M. Lee, K. M. Kee, C. Y. Lin, C. L. Chen, T. Y. Chen, Y. J. Huang, and S. N. Lu, “The efficacy of treatment schedules according to Barcelona Clinic Liver Cancer staging for hepatocellular carcinoma - Survival analysis of 3892 patients,” Eur J Cancer, vol. 44, no. 7, pp. 1000-6, May, 2008.
[23]P. H. Chen, Y. C. Lin, H. P. Tu, S. L. Chiang, A. M. Ko, C. L. Hsu, Y. F. Chang, and Y. C. Ko, “Important prognostic factors for the long-term survival of subjects with primary liver cancer in Taiwan: a hyperendemic area,” European journal of cancer (Oxford, England : 1990), vol. 43, no. 6, pp. 1076-1084, 04/, 2007.
[24]D. W. David W. Aha, “Weighting features,” Proceedings of the First International Conference on Case-Based Reasoning, no. 1010, 1995-01-01, 1995.
[25]P. Romanski, “Selecting attributes,” Package ‘FSelector’, vol. 2009, no. 10-29, 2009.
[26]I. Kononenko, “Estimating attributes: Analysis and extensions of RELIEF,” Machine Learning: ECML-94, no. 784, 1994/01/01, 1994.
[27]A. Liaw, and M. Wiener, “Classification and Regression by randomForest,” R news, vol. 2, no. 3, pp. 18-22, 2002.
[28]Wikipedia. "Spearman''s rank correlation coefficient," http://en.wikipedia.org/wiki/Spearman''s_rank_correlation_coefficient.
[29]Wikipedia. "Pearson product-moment correlation coefficient," http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
[30]R. i. b. M. B. K. Algorithm by Witold R. Rudnicki, Boruta -- a tool for finding significant attributes in information systems, 2012-02-22.
[31]M. B. Kursa, and W. R. Rudnicki, “Feature selection with the boruta package,” Journal of Statistical Software, vol. 36, no. 11, 2010.
[32]J. McDonald, Handbook of Biological Statistics: Sparky House Publishing, 2009.
[33]L. Breiman, “Random Forests,” Machine Learning vol. 45, no. 1, pp. 5-32, 2001.
[34]M. B. Kursa, A. Jankowski, and W. R. Rudnicki, “Boruta - A System for Feature Selection,” Fundam. Inf., vol. 101, no. 4, pp. 271-285, 2010.
[35]Kim H, Golub GH, Park H. Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 2005 Jan 15;21(2):187-98. PMID:ISI:000226308500007.
[36]Selbig J, Stacklies W, Redestig H, Scholz M, Walther D. pcaMethods - a bioconductor package providing PCA methods for incomplete data. Bioinformatics 2007 May 1;23(9):1164-7. PMID:ISI:000246773300018.